



'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BeitImageProcessor

class 							a__( unittest.TestCase     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=7					,   _UpperCAmelCase=3					,   _UpperCAmelCase=18					,   _UpperCAmelCase=30					,   _UpperCAmelCase=400					,   _UpperCAmelCase=True					,   _UpperCAmelCase=None					,   _UpperCAmelCase=True					,   _UpperCAmelCase=None					,   _UpperCAmelCase=True					,   _UpperCAmelCase=[0.5, 0.5, 0.5]					,   _UpperCAmelCase=[0.5, 0.5, 0.5]					,   _UpperCAmelCase=False					,   )							->							Tuple:
					snake_case__	     =size if size is not None else {'height': 20, 'width': 20}
					snake_case__	     =crop_size if crop_size is not None else {'height': 18, 'width': 18}
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =min_resolution
					snake_case__	     =max_resolution
					snake_case__	     =do_resize
					snake_case__	     =size
					snake_case__	     =do_center_crop
					snake_case__	     =crop_size
					snake_case__	     =do_normalize
					snake_case__	     =image_mean
					snake_case__	     =image_std
					snake_case__	     =do_reduce_labels






			def 						_lowercase  (     self						)							->							Optional[int]:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_reduce_labels": self.do_reduce_labels,
					}






def 				a  (     )       ->	int:
		snake_case__	     =load_dataset('hf-internal-testing/fixtures_ade20k'      ,					split='test'			)

		snake_case__	     =Image.open(dataset[0]['file']			)
		snake_case__	     =Image.open(dataset[1]['file']			)

		return image, map






def 				a  (     )       ->	Any:
		snake_case__	     =load_dataset('hf-internal-testing/fixtures_ade20k'      ,					split='test'			)

		snake_case__	     =Image.open(ds[0]['file']			)
		snake_case__	     =Image.open(ds[1]['file']			)
		snake_case__	     =Image.open(ds[2]['file']			)
		snake_case__	     =Image.open(ds[3]['file']			)

		return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: Any									=      BeitImageProcessor if is_vision_available() else None






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =BeitImageProcessingTester(self						)






			@property
			def 						_lowercase  (     self						)							->							Dict:
					return self.image_processor_tester.prepare_image_processor_dict()






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'do_resize'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'size'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'do_center_crop'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'center_crop'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'do_normalize'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'image_mean'						)						)
					self.assertTrue(hasattr(_UpperCAmelCase					,   'image_std'						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size					,   {'height': 20, 'width': 20}						)
					self.assertEqual(image_processor.crop_size					,   {'height': 18, 'width': 18}						)
					self.assertEqual(image_processor.do_reduce_labels					,   _UpperCAmelCase						)

					snake_case__	     =self.image_processing_class.from_dict(
					    self.image_processor_dict					,   size=42					,   crop_size=84					,   reduce_labels=_UpperCAmelCase						)
					self.assertEqual(image_processor.size					,   {'height': 42, 'width': 42}						)
					self.assertEqual(image_processor.crop_size					,   {'height': 84, 'width': 84}						)
					self.assertEqual(image_processor.do_reduce_labels					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Any:
					pass






			def 						_lowercase  (     self						)							->							Tuple:
					# Initialize image_processing
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					snake_case__	     =prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_UpperCAmelCase						)
					for image in image_inputs:
							self.assertIsInstance(_UpperCAmelCase					,   Image.Image						)

					# Test not batched input
					snake_case__	     =image_processing(image_inputs[0]					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )

					# Test batched
					snake_case__	     =image_processing(_UpperCAmelCase					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )






			def 						_lowercase  (     self						)							->							Any:
					# Initialize image_processing
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					snake_case__	     =prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_UpperCAmelCase					,   numpify=_UpperCAmelCase						)
					for image in image_inputs:
							self.assertIsInstance(_UpperCAmelCase					,   np.ndarray						)

					# Test not batched input
					snake_case__	     =image_processing(image_inputs[0]					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )

					# Test batched
					snake_case__	     =image_processing(_UpperCAmelCase					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )






			def 						_lowercase  (     self						)							->							Dict:
					# Initialize image_processing
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					snake_case__	     =prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_UpperCAmelCase					,   torchify=_UpperCAmelCase						)
					for image in image_inputs:
							self.assertIsInstance(_UpperCAmelCase					,   torch.Tensor						)

					# Test not batched input
					snake_case__	     =image_processing(image_inputs[0]					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )

					# Test batched
					snake_case__	     =image_processing(_UpperCAmelCase					,   return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape					,   (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )






			def 						_lowercase  (     self						)							->							Dict:
					# Initialize image_processing
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					snake_case__	     =prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_UpperCAmelCase					,   torchify=_UpperCAmelCase						)
					snake_case__	     =[]
					for image in image_inputs:
							self.assertIsInstance(_UpperCAmelCase					,   torch.Tensor						)
							maps.append(torch.zeros(image.shape[-2:]						).long()						)

					# Test not batched input
					snake_case__	     =image_processing(image_inputs[0]					,   maps[0]					,   return_tensors='pt'						)
					self.assertEqual(
					    encoding['pixel_values'].shape					,   (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(
					    encoding['labels'].shape					,   (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(encoding['labels'].dtype					,   torch.long						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 255						)

					# Test batched
					snake_case__	     =image_processing(_UpperCAmelCase					,   _UpperCAmelCase					,   return_tensors='pt'						)
					self.assertEqual(
					    encoding['pixel_values'].shape					,   (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(
					    encoding['labels'].shape					,   (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(encoding['labels'].dtype					,   torch.long						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 255						)

					# Test not batched input (PIL images)
					snake_case__     ,	snake_case__	     =prepare_semantic_single_inputs()

					snake_case__	     =image_processing(_UpperCAmelCase					,   _UpperCAmelCase					,   return_tensors='pt'						)
					self.assertEqual(
					    encoding['pixel_values'].shape					,   (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(
					    encoding['labels'].shape					,   (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(encoding['labels'].dtype					,   torch.long						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 255						)

					# Test batched input (PIL images)
					snake_case__     ,	snake_case__	     =prepare_semantic_batch_inputs()

					snake_case__	     =image_processing(_UpperCAmelCase					,   _UpperCAmelCase					,   return_tensors='pt'						)
					self.assertEqual(
					    encoding['pixel_values'].shape					,   (
					        2,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(
					    encoding['labels'].shape					,   (
					        2,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )					,   )
					self.assertEqual(encoding['labels'].dtype					,   torch.long						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 255						)






			def 						_lowercase  (     self						)							->							Tuple:
					# Initialize image_processing
					snake_case__	     =self.image_processing_class(**self.image_processor_dict						)

					# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
					snake_case__     ,	snake_case__	     =prepare_semantic_single_inputs()
					snake_case__	     =image_processing(_UpperCAmelCase					,   _UpperCAmelCase					,   return_tensors='pt'						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 150						)

					snake_case__	     =True
					snake_case__	     =image_processing(_UpperCAmelCase					,   _UpperCAmelCase					,   return_tensors='pt'						)
					self.assertTrue(encoding['labels'].min().item() >= 0						)
					self.assertTrue(encoding['labels'].max().item() <= 255						)









'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




